Inflammatory and Autoimmune Diseases
Autoimmune diseases are characterized by the body's immune responses being directed against its own tissues, resulting in inflammation and destruction. A wide range of degenerative diseases are caused as a result. For example, immune dysfunction can cause immune responsive cells to attack the linings of the joints, resulting in rheumatoid arthritis, or prompt defectively functioning immune cells to attack the insulin-producing islet cells of the pancreas, resulting in insulin-dependent diabetes.
There is a whole class of degenerative diseases that are caused by changes in the immune system that result in the immune system attacking normal cells in the body. Any disease is considered autoimmune if antibodies or cytotoxic cells are directed against self-antigens in the body's own tissues. Diseases such as lupus erythematosus, autoimmune hepatitis, diabetes, pancreatitis, and rheumatoid arthritis can develop and become dangerous diseases requiring drastic measures to control and correct. Allergy is also the result of disordered immune function. Additionally, there are other diseases that may be the result of autoimmune dysfunction, such as multiple sclerosis.
Studies in both humans and in animal models of specific disorders suggest that polymorphisms of multiple genes are involved in conferring either a predisposition to or protection from autoimmune diseases. Genes encoding polymorphic proteins that regulate immune responses or the rates and extent of metabolism of certain chemical structures have been the focus of much of the research regarding genetic susceptibility. Twin studies of rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type I diabetes, and multiple sclerosis (MS) indicate that disease concordance in monozygotic twins is 4 or more times higher than in dizygotic twins. Strong familial associations (odds ratio ranging from 5-10) are seen in studies of MS, type I diabetes, Graves disease, discoid lupus, and SLE. Familial association studies have also reported an increased risk of several systemic autoimmune diseases among relatives of patients with a systemic autoimmune disease. This association may reflect a common etiologic pathway with shared genetic or environmental influences among these diseases. Recent genome-wide searches in RA, SLE, and MS provide evidence for multiple susceptibility genes involving major histocompatibility complex (MHC) and non-MHC loci; there is also evidence that many autoimmune diseases share a common set of susceptibility genes.
Lupus
Lupus is a chronic, systemic, inflammatory form of Arthritis that is an autoimmune disease. Lupus is an autoimmune disease. Essentially this means that the body attacks itself. Arthritic symptoms of joint pain and swelling are common with Lupus but inflammation and injury can occur in other body tissues and organs. Lupus is an unpredictable disease characterized by periods of flare-up and remission. Although, there is no cure, Lupus is treatable. Eight to ten times more women suffer from Lupus than men.
Asthma
Between 12 and 15 million people, including close to five million children, in the United States have asthma. Asthma is a chronic disease in which airflow in and out of the lungs may be blocked by muscle squeezing, swelling and excess mucus. Patients with asthma may respond to factors in the environment, called triggers, which do not affect non-asthmatics. In response to a trigger, an asthmatic's airways become narrowed and inflamed, resulting in wheezing and/or coughing symptoms.
Experimental results indicate linkage of asthma to a region on chromosome 12. (Wilkinson, J., et al., Genomics 53: 251-259, 1998. PubMed ID): 9799590) Another region of linkage mapping was identified by Holroyd et al. (Genomics 52: 233-235, 1998. PubMed ID: 9782093). The researchers examined the long arm XY pseudoautosomal region for linkage to asthma, serum IgE, and bronchial hyperresponsiveness. In 57 Caucasian families, multipoint nonparametric analyses provided evidence for linkage between DXYS154 and bronchial hyperresponsiveness (P=0.000057) or asthma (P=0.00065). This genomic region is approximately 320 kb long and contains the interleukin-9 receptor gene (300007). These results suggested that a gene controlling asthma and bronchial hyperresponsiveness may be located in this region and that IL9R is a candidate.
Analyses of sib pairs showed linkage of bronchial hyperresponsiveness with several genetic markers on chromosome 5q, including D5S436. The results were interpreted as indicating that a gene governing bronchial hyperresponsiveness is located near a major locus that regulates serum IgE levels on 5q. (Postma, D. S., et al., New Eng. J. Med. 333: 894-900, 1995. PubMed ID: 7666875)
Genes identified in the art as being associated with asthma include, but are not limited to, genes encoding the proteins listed in the following table. The table lists the art-known protein name, an NCBI GI identifying number for the protein, and an NCBI PubMed PMID identifying number and citation information for a reference or references associating the gene or protein with asthma.
NameGIPMIDCitationalpha-1-antichymotrypsin45018431351206Poller W, et al., Lancet Jun. 20,1992; 339(8808): 1538.adrenergic, alpha-1A-,45019611970822,Schwinn DA, et al., J Biol Chem.receptor8396931May 15, 1990; 265(14): 8183-9.Hirasawa A, et al., Biochem BiophysRes Commun. Sep. 15, 1993; 195(2): 902-9.adrenergic, beta-2-, receptor,45019693034889Kobilka BK, et al., J Biol Chem.surfaceMay 25, 1987; 262(15): 7321-7.interleukin 4 receptor45576691679753,Pritchard MA, et al., Genomics.2307934July 1991 ; 10(3): 801-6.Idzerda RL, et al., J Exp Med.Mar. 1, 1990; 171(3): 861-73.
Inflammatory Bowel Disease
In developed countries as many as two individuals in every thousand suffer from inflammatory bowel disease (ulcerative colitis and Crohn's disease). Inflammatory bowel disease—a collective term embracing both ulcerative colitis and Crohn's disease—is a significant health-care problem affecting between 0.1% and 0.2% of the population in developed countries. These important and disabling conditions are characterized by diarrhea, pain, and other intestinal symptoms, and by lifelong relapses. Ulcerative colitis is confined to the mucosal layer of the large bowel, whereas Crohn's disease can affect any portion of the intestinal tract. The pathogenesis of inflammatory bowel disease is complex but appears to involve interactions among three essential ingredients: host genetic susceptibility, intestinal bacteria, and the gut mucosal immune response. Despite impressive advances in drug therapy, most treatment strategies have two major limitations: first, they suppress or otherwise alter the host immune response, thereby neglecting the contribution of enteric bacterial microflora to disease pathogenesis; and second, current immunomodulatory drugs lack organ specificity, affecting both mucosal and systemic host responses and resulting in unpleasant side effects.
The immune response in the intestinal mucosa is conditioned by the indigenous bacterial microflora with which it exchanges regulatory signals. In susceptible individuals, inflammatory bowel disease arises when the immune system misperceives danger within the normal gut microflora and interprets the harmless enteric bacteria as pathogenic invaders; this leads to a breakdown in the normal regulatory constraints on mucosal immune responses to enteric bacteria. The profile of cytokines generated within the gut mucosa, which is genetically controlled and may differ from person to person, determines the features of the inflammatory process. Crohn's disease is associated with a predominance of type 1 helper T cell (TH1) cytokines such as tumor necrosis factor-a (TNF-a), interferon-g and IL-12, whereas type 2 helper T cell (TH2) cytokines such as IL-4 and particularly IL-S are usually found in ulcerative colitis. Despite redundancy among mediators of inflammation, a hierarchy of importance has emerged with TNF-a as a key effector and regulatory molecule in TH1 responses. This explains the rationale and efficacy of therapies that are designed to manipulate the intestinal cytokine milieu, for example, the treatment of Crohn's disease patients with antibodies to TNF-a.
Genes identified in the art as being associated with inflammatory bowel disease include, but are not limited to, genes encoding the proteins listed in the following table. The table lists the art-known protein name, an NCBI GI identifying number for the protein, and an NCBI PubMed PMID identifying number and citation information for a reference or references associating the gene or protein with inflammatory bowel disease.
NameGIPMIDCitationinterleukin 2 receptor, beta45046652785715,Hatakeyama M, et al., Science.10940280,May 5, 1989; 244(4904): 551-6.Frieri G, et al., Gut. September 2000; 47(3): 410-4.interleukin 10 receptor,45046338120391,Liu Y, et al., J Immunol. Feb. 15,alpha109587821994; 152(4): 1821-9.Steidler L, et al., Science. Aug. 25,2000; 289(5483): 1352-5.gamma-48852711378736,Courtay C, et al., Biochemglutamyltransferase 11968061Pharmacol. Jun. 23,2568315,1992; 43(12): 2527-33.2885259,Pawlak A, et al., J Biol Chem.2904146,Feb. 25, 1990; 265(6): 3256-62.2907498,Goodspeed DC, et al., Gene. Mar. 15,7689219,1989; 76(1): 1-9.7906515,Bulle F, et al., Hum Genet. July8104826,1987; 76(3): 283-6.9738450,Rajpert-De Meyts E, et al., Proc10392451,Natl Acad Sci USA. December1988; 85(23): 8840-4.1348588Sakamuro D, et al., Gene. Dec. 15,1988; 73(1): 1-9.Wetmore LA, et al., Proc NatlAcad Sci USA. Aug. 15,1993; 90(16): 7461-5.Courtay C, et al., Biochem J. Feb. 1,1994; 297 (Pt 3): 503-8.Diederich M, et al., FEBS Lett.Oct. 11, 1993; 332(1-2): 88-92.Leh H, et al., FEBS Lett. Aug. 28,1998; 434(1-2): 51-6.Chikhi N, et al., Comp BiochemPhysiol B Biochem Mol Biol. April1999; 122(4): 367-80. Review.D'Argenio G, et al., Scand JGastroenterol. 1992; 27(2): 111-4.
Diabetes Type 1
Diabetes is a chronic disease that requires long-term medical attention to limit the development of its devastating complications and manage them when they do occur. It is a disproportionately expensive disease; patients diagnosed with diabetes accounted for 4.6% of the U.S. population, yet were responsible for 14.6% of all direct care expenditures in 1994. This chapter focuses on the ED evaluation and treatment of the acute and chronic complications of diabetes other than those directly associated with hypoglycemia and severe metabolic disturbances, such as diabetic ketoacidosis (DKA) and hyperosmolar nonketotic syndrome (HNKS).
Type 1 diabetes generally occurs in younger, lean patients and is characterized by the marked inability of the pancreas to secrete insulin, due to autoimmune destruction of the beta-cells. The distinguishing characteristic of a patient with Type 1 diabetes is that if insulin is withdrawn, ketosis and eventually ketoacidosis develop. These patients are therefore insulin-dependent (i.e., insulin is life-sustaining) since they produce no endogenous insulin.
The early symptoms of IDDM can be gradual or sudden. They include frequent urination (particularly at night), increased thirst, unexplained weight loss (in spite of increased appetite), and extreme tiredness. These symptoms are caused by the build-up of sugar in the blood and its loss in the urine.
To eliminate sugar in the urine, the kidney “borrows” water from the body. The loss of this extra sugar and water in the urine results in dehydration, which causes increased thirst. In addition to causing high blood glucose, the lack of insulin causes the body to break down stored fats and proteins. As fats are broken down, the body can convert these fats into waste products called ketones. If ketone production is excessive, abnormal amounts of ketones in the blood can spill into the urine. If blood ketone levels rise too high, a life-threatening condition called ketoacidosis can develop, which requires immediate medical attention. Symptoms of ketoacidosis include abdominal pain, vomiting, rapid breathing, extreme tiredness, and drowsiness.
Diabetes requires constant attention and daily care to keep blood sugar levels in balance. Injecting insulin, following a diet, exercising, and testing blood sugar are some of the day-to-day requirements. To feel good and stay healthy, a person with IDDM must follow a daily management routine. For this reason, diabetes is often referred to as a “24-hour” disease.
Diabetes can affect many parts of the body. Over time, it can damage a person's kidneys, eyes, nerves, and heart. These long-term complications can result in kidney disease, vision loss, nerve damage, heart attack, and other problems.
The cause of type 1 diabetes is multifactorial and includes the effects of perhaps as many as 11 to 16 genes, interacting with unknown environmental agents. However, the IDDM1 locus (including the HLA-DR and DQ genes) is the only major genetic determinant, accounting for up to 50% of the familial clustering of type 1 diabetes. Among non-HLA loci, the IDDM2 locus near the insulin gene on chromosome 11p is currently partially characterized.
The remaining loci, reported from genome screening of affected sibling pairs, are only tentatively mapped to wide regions on several chromosomes. It may turn out that some of these findings are falsely-positive, and for the loci truly associated with IDDM, it may take years before the actual genes and their functions are known. However, even with the incomplete information available today, HLA-DR and DQ typing can help to predict who is at risk.
Genes identified in the art as being associated with IDDM include, but are not limited to, genes encoding the proteins listed in the following table. The table lists the art-known protein name, an NCBI GI identifying number for the protein, and an NCBI PubMed PMID identifying number and citation information for a reference or references associating the gene or protein with IDDM.
NameGIPMIDCitationconstitutive glucose121751,2834252,Fukumoto H, et al., Diabetes. Maytransporter57300513170580,1988; 37(5): 657-61.3839598,Kayano T, et al., J Biol Chem.3028891,Oct. 25, 1988; 263(30): 15245-8.Mueckler M, et al., Science.Sep. 6, 1985; 229(4717): 941-5.Shows TB, et al., Diabetes. April1987; 36(4): 546-9.low-affinity glucose1217563399500Fukumoto H, et al., Proc NatltransporterAcad Sci USA. August1988; 85(15): 5434-8.high-affinity glucose38784269851916Science. Dec. 11,1998; transporter11; 282(5396): 2012-8. Review.insulin-responsive glucose1217611397719,Buse JB, et al., Diabetes. Novembertransporter1918382,1992; 41(11): 1436-45.2656669,Kusari J, et al., J Clin Invest.7916714October 1991; 88(4): 1323-30.Fukumoto H, et al., J Biol Chem.May 15, 1989; 264(14): 7776-9.Chiaramonte R, et al., Gene.Aug. 25, 1993; 130(2): 307-8.fructose transporter1217641695905Kayano T, et al., J Biol Chem.Aug. 5, 1990; 265(22): 13276-82.insulin-responsive-O755589553086,Advani RJ, et al., J Biol Chem.aminopeptidase*,9571206,Apr. 24, 1998; 273(17): 10317-24.synaptobrevin (also10036234Tang BL, et al., Biochem Biophysknown as vesicle-Res Commun. Apr. 17,associated membrane1998; 245(2): 627-32.protein-2)Valdez AC, et al., J Cell Sci. March1999; 112 (Pt 6): 845-54.small guanosine17078861286667,Rasmussen HH, et al.,triphosphate-binding7543319,Electrophoresis. Decemberprotein7585614,1992; 13(12): 960-9.7849400,Bachner D, et al., Hum Mol Genet.9620768,April 1995; 4(4): 701-8.9668174Nishimura N, et al., Cancer Res.Nov. 15, 1995; 55(22): 5445-50.Sedlacek Z, et al., MammGenome. October 1994; 5(10): 633-9.D'Adamo P, et al., Nat Genet.June 1998; 19(2): 134-9.Bienvenu T, et al., Hum MolGenet. August 1998; 7(8): 1311-5.syntaxin, also known as30417378206394,Li H, et al., Gene. Jun. 10,target synaptosome-87603871994; 143(2): 303-4.associated proteinJagadish MN, et al., Biochem J.receptor, or t-SNAREAug. 1, 1996; 317 (Pt 3): 945-54.insulin124617,503234,Bell GI, et al., Nature. Nov. 29,45576711433291,1979; 282(5738): 525-7.1601997,Jorgensen AM, et al., J Mol Biol.1406615,Oct. 20, 1992; 227(4): 1146-63.6243748,Yano H, et al., J Clin Invest. June70272611992; 89(6): 1902-7.Chekhranova MK, et al., Mol Biol(Mosk). May-June 1992; 26(3): 596-600. Russian.Bell GI, et al., Nature. Mar. 6, 1980; 284(5751): 26-32.Harper ME, et al., Proc Natl AcadSci USA. July 1981; 78(7): 4458-60.insulin receptor substrate547738,1311924,Nishiyama M, et al., Biochem50318058104271,Biophys Res Commun. Feb. 28,8513971,1992; 183(1): 280-5.8599766,Almind K, et al., Lancet. Oct. 2,8723689,1993; 342(8875): 828-32.8513971Araki E, et al., Diabetes. July1993; 42(7): 1041-54.Zhou MM, et al., Nat Struct Biol.April 1996; 3(4): 388-93.Esposito DL, et al., Hum Mutat.1996; 7(4): 364-6.Araki E, et al., Diabetes. July1993; 42(7): 1041-54.phosphatidylinositol 3-1709513,7624799Stoyanov B, et al., Science. Aug. 4,kinase45058031995; 269(5224): 690-3.5′-AMP-activated kinase3023235bradykinin125512,2989293,Takagaki Y, et al.,45048932989294,J Biol Chem. Jul. 15,4952632,1985; 260(14): 8601-9.6441591Kitamura N, et al., J Biol Chem.Jul. 15, 1985; 260(14): 8610-7.Pierce JV. Fed Proc. January-February 1968; 27(1): 52-7.Ohkubo I, et al., Biochemistry.Nov. 20, 1984; 23(24): 5691-7.bradykinin receptor1787258,9278503,Blattner FR, et al., Science. Sep. 5,450239180637971997; 277(5331): 1453-74.Menke JG, et al., J Biol Chem.Aug. 26, 1994; 269(34): 21583-6.IGF-I receptor124240,1316909,Abbott AM, et al., J Biol Chem.45576651711844,May 25, 1992; 267(15): 10759-63.2877871,Cooke DW, et al., Biochem8247543,Biophys Res Commun. Jun. 281316909,1991; 177(3): 1113-20.2877871,Ullrich A, et al., EMBO J. October3003744,1986; 5(10): 2503-12.3107886,Lee ST, et al., Oncogene. December8710868,1993; 8(12): 3403-10.9745438Abbott AM, et al., J Biol Chem.May 25, 1992; 267(15): 10759-63.Ullrich A, et al., EMBO J. October1986; 5(10): 2503-12.Flier JS, et al., Proc Natl Acad SciUSA. February 1986; 83(3): 664-8.Francke U, et al., Cold SpringHarb Symp Quant Biol. 1986; 51 Pt2: 855-66.Werner H, et al., Proc Natl AcadSci USA. Aug. 6, 1996; 93(16): 8318-23.Grant ES, et al., J Clin EndocrinolMetab. September 1998; 83(9): 3252-7.IGF-II receptor127296,2957598,Morgan DO, et al., Nature. Sep. 24-30,45046112963003,1987; 329(6137): 301-7.2957598,Oshima A, et al.,J Biol Chem. Feb. 15,1988; 263(5): 2553-62.Morgan DO, et al.,Nature. Sep. 24-30,1987; 329(6137): 301-7.leptin730218,7499240,Isse N, et al., J Biol Chem. Nov. 17,455771577896541995; 270(46): 27728-33.Masuzaki H, et al., Diabetes. July1995; 44(7): 855-8.thyroid autoantigen 70 kD45038412917966Chan JY, et al., J Biol Chem.(Ku antigen)Mar. 5, 1989; 264(7): 3651-4.thyroglobulin45074593595599Malthiery Y, Eur J Biochem. Jun. 15,1987; 165(3): 491-8.Berge-Lefranc JL, et al., HumGenet. 1985; 69(1): 28-31.thyroid hormone receptor-488564710198638Ito M, et al., Mol Cell. Marchassociated protein1999; 3(3): 361-70.complex componentthyroid hormone receptor57300538611617,Nielsen MS, et al., Biochimcoactivating protein9368056Biophys Acta. Apr. 10,1996; 1306(1): 14-6.Monden T, et al., J Biol Chem.Nov. 21, 1997; 272(47): 29834-41.thyroid hormone76576219443824Fujita K, et al., J Biochemsulfotransferase(Tokyo). November 1997; 122(5):1052-61.insulin-like 3 (Leydig cell)50318038020942Burkhardt E, et al., Genomics.Mar. 1, 1994; 20(1): 13-9.Genes Associated with Inflammatory Autoimmune Disease
Genes identified in the art as being associated with inflammatory autoimmune diseases include, but are not limited to, genes encoding the proteins listed in the following table. The table lists the art-known protein name, an NCBI GI identifying number for the protein, and an NCBI PubMed PMID identifying number and citation information for a reference or references associating the gene or protein with inflammatory autoimmune diseases.
NameGIPMIDCitationGranulocyte colony138971,Briedis DJ, Lamb RA. J Virol.stimulating factorNP_000751.1April 1982; 42(1): 186-93.PMID: 6283137; UI: 82216985Fukunaga R, Seto Y, Mizushima S,Nagata S. Proc Natl Acad Sci USA.November 1990; 87(22): 8702-6.PMID: 1701053; UI: 91062348Macrophage colony164770Oikawa S, Imai M, Inuzuka C,stimulating factor ITawaragi Y, Nakazato H, Matsuo H.Biochem Biophys Res Commun.Nov. 15, 1985; 132(3): 892-9.PMID: 2934062; UI: 86076957Granulocyte-macrophage306250Robertson BH, Jansen RW,Khanna B, Totsuka A, Nainan OV,Siegl G, Widell A, Margolis HS,Isomura S, Ito K, et al.J Gen Virol. June 1992; 73(Pt 6): 1365-77.PMID: 1318940; UI: 92300330Mast/stem cell growth4557695,Yarden Y, Kuang WJ, Yang-Fengfactor receptorNP_000213.1T, Coussens L, Munemitsu S, DullTJ, Chen E, Schlessinger J,Francke U, Ullrich A.EMBO J. November 1987; 6(11): 3341-51.PMID: 2448137; UI: 88111521Andre C, Hampe A, Lachaume P,Martin E, Wang XP, Manus V, HuWX, Galibert F. Genomics. Jan. 15,1997; 39(2): 216-26.PMID: 9027509; UI: 97179223Interferon-gammaNP_000407.1Le Coniat M, Alcaide-Loridan C,receptor alpha chainFellous M, Berger R. Hum Genet.December 1989; 84(1): 92-4. PMID:2532616; UI: 90109176Aguet M, Dembic Z, Merlin G.Cell. Oct. 21, 1988; 55(2): 273-80.PMID: 2971451; UI: 89003065Analogs of macrophage123114U.S. Pat. No. 5,948,892stimulating proteinHan S, Stuart LA, Degen SJ.Biochemistry. Oct. 8,1991; 30(40): 9768-80. PMID:1655021; UI: 92002016Anti-inflammatory115956U.S. Pat. No. 5,869,055CD14 polypeptidesFerrero E, Goyert SM. NucleicAcids Res. May 11, 1988;16(9): 4173. No abstractavailable. PMID: 2453848; UI:88234022Simmons DL, Tan S, Tenen DG,Nicholson-Weller A, Seed B.Blood. January 1989; 73(1): 284-9.PMID: 2462937; UI: 89088540adenosine deaminase,4501917Wang Y, Zeng Y, Murray JM,RNA-specific, isoformNishikura K. J Mol Biol. Nov. 24,ADAR-a1995; 254(2): 184-95. PMID:7490742; UI: 96083821Patterson JB, Samuel CE. Mol CellBiol. October 1995; 15(10): 5376-88.PMID: 7565688; UI: 96009564ADP-ribosyltransferase4501955Cherney BW, McBride OW, Chen(NAD+; poly (ADP-DF, Alkhatib H, Bhatia K, Hensleyribose) polymerase)P, Smulson ME. Proc Natl AcadSci USA. December 1987; 84(23): 8370-4.PMID: 2891139; UI: 88068596aggrecan 1, isoform 14501991Doege KJ, Sasaki M, Kimura T,Yamada Y. J Biol Chem. Jan. 15,1991; 266(2): 894-902. PMID:1985970; UI: 91093289Baldwin CT, Reginato AM,Prockop DJ.J Biol Chem. Sep. 25, 1989;264(27): 15747-50. PMID:2789216; UI: 89380154Barry FP, Neame PJ, Sasse J,Pearson D.Matrix Biol. August 1994; 14(4): 323-8.PMID: 7827755; UI: 95128522Korenberg JR, Chen XN, DoegeK, Grover J, Roughley PJ.Genomics. May 1993; 16(2): 546-8.No abstract available.PMID: 8314595; UI: 93300539A kinase (PRKA) anchor4502015Lin RY, Moss SB, Rubin CS. Jprotein 1Biol Chem. Nov. 17, 1995;270(46): 27804. PMID:7499250; UI: 96070913Trendelenburg G, Hummel M,Riecken EO, Hanski C. BiochemBiophys Res Commun. Aug. 5,1996; 225(1): 313-9.PMID: 8769136; UI: 96332447A kinase (PRKA) anchor4502017Mohapatra B, Verna S, Shankar S,protein 4Suri A. Biochem Biophys ResCommun. Mar. 17, 1998;244(2): 540-5.PMID: 9514854; UI: 98189217Turner RM, Johnson LR, Haig-Ladewig L, Gerton GL, Moss SB.J Biol Chem. Nov. 27, 1998;273(48): 32135-41. PMID:9822690; UI: 99041985arachidonate 12-4502051Izumi T, Hoshiko S, Radmark O,lipoxygenaseSamuelsson B. Proc Natl Acad SciUSA. October 1990; 87(19): 7477-81.PMID: 2217179; UI: 91017529arachidonate 15-4502055Sigal E, Craik CS, Highland E,lipoxygenaseGrunberger D, Costello LL, DixonRA, Nadel JA. Biochem BiophysRes Commun. Dec. 15, 1988;157(2): 457-64.PMID: 3202857; UI: 89076270alkaline phosphatase,4502063Weiss MJ, Ray K, Henthorn PS,liver/bone/kidneyLamb B, Kadesch T, Harris H. JBiol Chem. Aug. 25,1988; 263(24): 12002-10. PMID:3165380; UI: 88298884Weiss MJ, Henthorn PS, LaffertyMA, Slaughter C, Raducha M,Harris H. Proc Natl Acad Sci USA.October 1986; 83(19): 7182-6.PMID: 3532105; UI: 87016911annexin VIII4502113Chang KS, Wang G, Freireich EJ,Daly M, Naylor SL, Trujillo JM,Stass SA. Blood. Apr. 1,1992; 79(7): 1802-10. PMID: 1313714;UI: 92216091Chambers JA, Gardner E,Hauptmann R, Ponder BA,Mulligan LM. Hum Mol Genet.October 1992; 1(7): 550. PMID:1364010; UI: 93338455Sarkar A, Yang P, Fan YH, MuZM, Hauptmann R, Adolf GR,Stass SA, Chang KS. Blood. Jul. 1,1994; 84(1): 279-86. PMID:8018923; UI: 94289712acyloxyacyl hydrolase4502115Hagen FS, Grant FJ, Kuijper JL,large subunitSlaughter CA, Moomaw CR, OrthK, O'Hara PJ, Munford RS.Biochemistry. Aug. 27,1991; 30(34): 8415-23. PMID:1883828; UI: 91355197Whitmore TE, Mathewes SL,O'Hara PJ, Durnam DM.Genomics. May 15, 1994;21(2): 457-8. PMID: 8088847;UI: 94375080arylsulfatase D isoform a4502239Franco B, Meroni G, Parenti G,Levilliers J, Bernard L, Gebbia M,Cox L, Maroteaux P, Sheffield L,Rappold GA, et al. Cell. 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SNPs
The genomes of all organisms undergo spontaneous mutation in the course of their continuing evolution, generating variant forms of progenitor sequences (Gusella, Ann. Rev. Biochem. 55, 831-854 (1986)). The variant form may confer an evolutionary advantage or disadvantage relative to a progenitor form or may be neutral. In some instances, a variant form confers a lethal disadvantage and is not transmitted to subsequent generations of the organism. In other instances, a variant form confers an evolutionary advantage to the species and is eventually incorporated into the DNA of many or most members of the species and effectively becomes the progenitor form. Additionally, the effect of a variant form may be both beneficial and detrimental, depending on the circumstances. For example, a heterozygous sickle cell mutation confers resistance to malaria, but a homozygous sickle cell mutation is usually lethal. In many instances, both progenitor and variant form(s) survive and co-exist in a species population. The coexistence of multiple forms of a sequence gives rise to polymorphisms, such as SNPs.
The reference allelic form is arbitrarily designated and may be, for example, the most abundant form in a population, or the first allelic form to be identified, and other allelic forms are designated as alternative, variant or polymorphic alleles. The allelic form occurring most frequently in a selected population is sometimes referred to as the “wild type” form.
Approximately 90% of all polymorphisms in the human genome are single nucleotide polymorphisms (SNPs). SNPs are single base pair positions in DNA at which different alleles, or alternative nucleotides, exist in some population. The SNP position, or SNP site, is usually preceded by and followed by highly conserved sequences of the allele (e.g., sequences that vary in less than 1/100 or 1/1000 members of the populations). An individual may be homozygous or heterozygous for an allele at each SNP position. As defined by the present invention, the least frequent allele at a SNP position can have any frequency that is less than the frequency of the more frequent allele, including a frequency of less than 1% in a population. A SNP can, in some instances, be referred to as a “cSNP” to denote that the nucleotide sequence containing the SNP is an amino acid coding sequence.
A SNP may arise due to a substitution of one nucleotide for another at the polymorphic site. Substitutions can be transitions or transversions. A transition is the replacement of one purine nucleotide by another purine nucleotide, or one pyrimidine by another pyrimidine. A transversion is the replacement of a purine by a pyrimidine, or vice versa. A SNP may also be a single base insertion/deletion variant (referred to as “indels”). A substitution that changes a codon coding for one amino acid to a codon coding for a different amino acid is referred to as a non-synonymous codon change, or missense mutation. A synonymous codon change, or silent mutation, is one that does not result in a change of amino acid due to the degeneracy of the genetic code. A nonsense mutation is a type of non-synonymous codon change that results in the formation of a stop codon, thereby leading to premature termination of a polypeptide chain and a defective protein.
SNPs, in principle, can be bi-, tri-, or tetra-allelic. However, tri- and tetra-allelic polymorphisms are extremely rare, almost to the point of nonexistence (Brookes, Gene 234 (1999) 177-186). For this reason, SNPs are often referred to as “bi-allelic markers”, or “di-allelic markers”.
Causative SNPs are those SNPs that produce alterations in gene expression or in the expression or function of a gene product, and therefore are most predictive of a possible clinical phenotype. One such class includes SNPs falling within regions of genes encoding a polypeptide product, i.e. cSNPs. These SNPs may result in an alteration of the amino acid sequence of the polypeptide product (i.e., non-synonymous codon changes) and give rise to the expression of a defective or other variant protein. Furthermore, in the case of nonsense mutations, a SNP may lead to premature termination of a polypeptide product. Such variant products can result in a pathological condition, e.g., genetic disease. Examples of genes in which a polymorphism within a coding sequence gives rise to genetic disease include sickle cell anemia and cystic fibrosis. Causative SNPs do not necessarily have to occur in coding regions; causative SNPs can occur in any region that can ultimately affect the expression and/or activity of the protein encoded by the nucleic acid. Such gene areas include those involved in transcription, such as SNPs in promoter regions, in gene areas involved in transcript processing, such as SNPs at intron-exon boundaries that may cause defective splicing, or SNPs in mRNA processing signal sequences such as polyadenylation signal regions. For example, a SNP may inhibit splicing of an intron and result in mRNA containing a premature stop codon, leading to a defective protein. Consequently, SNPs in regulatory regions can have substantial phenotypic impact.
Some SNPs that are not causative SNPs nevertheless are in close association with, and therefore segregate with, a disease-causing sequence. In this situation, the presence of the SNP correlates with the presence of, or susceptibility to, the disease. These SNPs are invaluable for diagnostics and disease susceptibility screening.
Clinical trials have shown that patient response to treatment with pharmaceuticals is often heterogeneous. Thus there is a need for improved approaches to pharmaceutical agent design and therapy. SNPs can be used to help identify patients most suited to therapy with particular pharmaceutical agents (this is often termed “pharmacogenomics”). Pharmacogenomics can also be used in pharmaceutical research to assist the drug selection process. (Linder et al. (1997), Clinical Chemistry, 43, 254; Marshall (1997), Nature Biotechnology, 15, 1249; International Patent Application WO 97/40462, Spectra Biomedical; and Schafer et al. (1998), Nature Biotechnology, 16, 3.).
Population Genetics
Population Genetics is the study of how Mendel's laws and other genetic principles apply to entire populations. Such a study is essential to a proper understanding of the genetic basis of inflammatory and autoimmune disorders and SNP-based association studies and linkage disequilibrium mapping. Population genetics thus seeks to understand and to predict the effects of such genetic phenomena as segregation, recombination, and mutation; at the same time, population genetics must take into account such ecological and evolutionary factors as population size, patterns of mating, geographic distribution of individuals, migration and natural selection.
Linkage is the coinheritance of two or more nonallelic genes because their loci are in close proximity on the same chromosome, such that after meiosis they remain associated more often than the 50% expected for unlinked genes. During meiosis, there is a physical crossing over, it is clear that during the production of germ cells there is a physical exchange of maternal and paternal genetic contributions between individual chromatids. This exchange necessarily separates genes in chromosomal regions that were contiguous in each parent and, by mixing them with retained linear order, results in “recombinants”. The process of forming recombinants through meiotic crossing-over is an essential feature in the reassortment of genetic traits and is central to understanding the transmission of genes.
Recombination generally occurs between large segments of DNA. This means that contiguous stretches of DNA and genes are likely to be moved together. Conversely, regions of the DNA that are far apart on a given chromosome are more likely to become separated during the process of crossing-over than regions of the DNA that are close together.
It is possible to use polymorphic molecular markers, such as SNPs, to clarify the recombination events that take place during meiosis. They are used as position markers and regional identifying characters along chromosomes and can also be used to distinguish paternally derived gene regions from maternally derived gene regions.
The pattern of a set of markers along a chromosome is referred to as a “Haplotype”. Therefore sets of alleles on the same small chromosomal segment tend to be transmitted as a block through a pedigree. By analyzing the haplotypes in a series of offspring of parents whose haplotypes are known, it is possible to establish which parental segment of which chromosome was transmitted to which child. When not broken up by recombination, haplotypes can be treated for mapping purposes as alleles at a single highly polymorphic locus.
The existence of a preferential occurrence of a disease gene in association with specific alleles of linked markers, such as SNPs, is called “Linkage Disequilibrium”(LD). This sort of disequilibrium generally implies that most of the disease chromosomes carry the same mutation and the markers being tested are quite close to the disease gene. For example, there is considerable linkage disequilibrium across the entire HLA locus. The A3 allele is in LD with the B7 and B14 alleles, and as a result B7 and B14 are also highly associated with hemochromatosis. Thus, HLA typing alone can significantly alter the estimate of risk for hemochromatosis, even if other family members are not available for formal linkage analysis. Consequently, by using a combination of several markers surrounding the presumptive location of the gene, a haplotype can be determined for affected and unaffected family members.
SNP-Based Association Analysis and Linkage Disequilibrium Mapping
SNPs are useful in association studies for identifying particular SNPs, or other polymorphisms, associated with pathological conditions, such as inflammatory and autoimmune disorders. Association studies may be conducted within the general population and are not limited to studies performed on related individuals in affected families (linkage studies). An association study using SNPs involves determining the frequency of the SNP allele in many patients with the disorder of interest, such as inflammatory and autoimmune disorders, as well as controls of similar age and race. The appropriate selection of patients and controls is critical to the success of SNP association studies. Therefore, a pool of individuals with well-characterized phenotypes is extremely desirable. For example, blood pressure and heart rate can be correlated with SNP patterns in hypertensive individuals in whom these physiological parameters are known in order to find associations between particular SNP genotypes and known phenotypes. Significant associations between particular SNPs or SNP haplotypes and phenotypic characteristics can be determined by standard statistical methods. Association analysis can either be direct or LD based. In direct association analysis, causative SNPs are tested that are candidates for the pathogenic sequence itself.
In LD based SNP association analysis, random SNPs are tested over a large genomic region, possibly the entire genome, in order to find a SNP in LD with the true pathogenic sequence or pathogenic SNP. For this approach, high density SNP maps are required in order for random SNPs to be located close enough to an unknown pathogenic locus to be in linkage disequilibrium with that locus in order to detect an association. SNPs tend to occur with great frequency and are spaced uniformly throughout the genome. The frequency and uniformity of SNPs means that there is a greater probability, compared with other types of polymorphisms such as tandem repeat polymorphisms, that a SNP will be found in close proximity to a genetic locus of interest. SNPs are also mutationally more stable than tandem repeat polymorphisms, such as VNTRs. LD-based association studies are capable of finding a disease susceptibility gene without any a priori assumptions about what or where the gene is.
Currently, however, it is not feasible to do SNP association studies over the entire human genome, therefore candidate genes associated with inflammatory and autoimmune disorders are targeted for SNP identification and association analysis. The candidate gene approach uses a priori knowledge of disease pathogenesis to identify genes that are hypothesized to directly influence development of the disease. The candidate gene approach may focus on a gene that is directly targeted by a drug used to treat the disorder. To discover SNPs associated with an increased susceptibility to inflammatory and autoimmune disorders, candidate genes can be selected from systems physiologically implicated in the disease pathway. SNPs found in these genes are then tested for statistical association with disease in individuals who have the disease compared with appropriate controls. The candidate gene approach has the advantages of drastically reducing the number of candidate SNPs, and the number of individuals, that need to be typed, compared with LD-based association studies of random SNPs over large areas of, or complete, genomes. Furthermore, in the candidate gene approach, no assumptions are made about the extent of LD over any particular area of the genome.
Combined with the use of a high density map of appropriately spaced, sufficiently informative SNP markers, association studies, including linkage disequilibrium-based genome wide association studies, will enable the identification of most genes involved in complex disorders, such as inflammatory and autoimmune disorders. This will enhance the selection of candidate genes most likely to contain causative SNPs associated with a particular disease. All of the SNPs disclosed by the present invention can be employed as part of genome-wide association studies or as part of candidate gene association studies.
The present invention advances the state of the art and provides commercially useful embodiments by providing previously unidentified SNPs in genes known in the art to be associated with inflammatory and autoimmune disorders.